diff --git a/Workshop_notebooks/Day1_notebook/logistic_regression.ipynb b/Workshop_notebooks/Day1_notebook/logistic_regression.ipynb new file mode 100644 index 0000000..cfb9783 --- /dev/null +++ b/Workshop_notebooks/Day1_notebook/logistic_regression.ipynb @@ -0,0 +1,194 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 134, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": {}, + "outputs": [], + "source": [ + "from GetData.read_data import get_stock_data\n", + "from _datetime import datetime" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": {}, + "outputs": [], + "source": [ + "Amazon = get_stock_data(name = 'AMZN', start = datetime(2017, 1, 1), end=datetime(2019, 1, 1))" + ] + }, + { + "cell_type": "code", + "execution_count": 178, + "metadata": {}, + "outputs": [], + "source": [ + "y = 1 * ((Amazon.Close - np.mean(Amazon.Close))>0)" + ] + }, + { + "cell_type": "code", + "execution_count": 179, + "metadata": {}, + "outputs": [], + "source": [ + "" + ] + }, + { + "cell_type": "code", + "execution_count": 179, + "metadata": {}, + "outputs": [], + "source": [ + "[train_data_x, test_data_x, train_data_y, test_data_y] = train_test_split(Amazon.Close, y, test_size=0.3, random_state=5)" + ] + }, + { + "cell_type": "code", + "execution_count": 180, + "metadata": {}, + "outputs": [], + "source": [ + "LG = LogisticRegression(solver='lbfgs')" + ] + }, + { + "cell_type": "code", + "execution_count": 181, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n intercept_scaling=1, max_iter=100, multi_class='warn',\n n_jobs=None, penalty='l2', random_state=None, solver='lbfgs',\n tol=0.0001, verbose=0, warm_start=False)\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "train_x = np.asarray(train_data_x).reshape(-1, 1)\n", + "test_x = np.asarray(test_data_x).reshape(-1, 1)\n", + "train_y = np.asarray(train_data_y)\n", + "test_y = np.asarray(test_data_y)\n", + "estimator = LG.fit(train_x, train_y)\n", + "print(estimator)" + ] + }, + { + "cell_type": "code", + "execution_count": 192, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 1 1\n 1 1 0 0 0 1 1 0 0 0 1 0 0 0 1 0 1 1 1 0 1 1 0 1 1 1 1 0 1 0 0 1 0 0 1 1 0\n 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0]\n" + ] + } + ], + "source": [ + "y_hat = estimator.predict(test_x)\n", + "print(y_hat)" + ] + }, + { + "cell_type": "code", + "execution_count": 193, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "100.0" + ] + }, + "execution_count": 193, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "accuracy = np.mean(test_y == y_hat) * 100\n", + "accuracy" + ] + }, + { + "cell_type": "code", + "execution_count": 197, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 197, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.plot(range(0, len(y_hat)), y_hat)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +}